{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9fd54a32",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/llm/openai.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e3a8796-edc8-43f2-94ad-fe4fb20d70ed",
   "metadata": {},
   "source": [
    "# Baseten Embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "081d07d2",
   "metadata": {},
   "source": [
    "This guide how to use performant open source embeddings and rerankers in the Baseten [library](https://www.baseten.co/library/tag/embedding/). First we need to install LlamaIndex and the Baseten dependencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f6f8702",
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install llama-index llama-index-embeddings-baseten"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b007403c-6b7a-420c-92f1-4171d05ed9bb",
   "metadata": {},
   "source": [
    "Start a dedicated endpoint of your choice with the embedding you want [here](https://www.baseten.co/library/tag/embedding/) and paste your Baseten API key below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23df96de",
   "metadata": {},
   "outputs": [],
   "source": [
    "from llama_index.embeddings.baseten import BasetenEmbedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1241728",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "# Using dedicated endpoint\n",
    "embed_model = BasetenEmbedding(\n",
    "    model_id=\"BASETEN_MODEL_ID\",  # 8 character string\n",
    "    api_key=\"BASETEN_API_KEY\",\n",
    ")\n",
    "\n",
    "# Single embedding\n",
    "embedding = embed_model.get_text_embedding(\"Hello, world!\")\n",
    "\n",
    "# Batch embeddings\n",
    "embeddings = embed_model.get_text_embedding_batch(\n",
    "    [\"Hello, world!\", \"Goodbye, world!\"]\n",
    ")\n",
    "print(embeddings)"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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    "name": "ipython",
    "version": 3
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   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3"
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 },
 "nbformat": 4,
 "nbformat_minor": 5
}
